Prism refraction search: a novel physics-based metaheuristic algorithm

• Published in: The Journal of supercomputing Vol. 80; no. 8; pp. 10746 - 10795
• Main Authors: Kundu, Rohit, Chattopadhyay, Soumitri, Nag, Sayan, Navarro, Mario A., Oliva, Diego
• Format: Journal Article
• Language: English
• Published: New York Springer US 01-05-2024; Springer Nature B.V
• Subjects: Algorithms; Compilers; Computer Science; Design engineering; Design standards; Geometrical optics; Heuristic methods; Interpreters; Light refraction; Optimization; Physical optics; Processor Architectures; Programming Languages; Statistical tests; Single solution; Metaheuristic; Prism refraction; Physics-inspired; Single-objective optimization; Continuous search space
• ISSN: 0920-8542; 1573-0484
• DOI: 10.1007/s11227-023-05790-3

Single-solution-based optimization algorithms are computationally cheap yet powerful methods that can be used on various optimization tasks at minimal processing expenses. However, there is a considerable shortage of research in this domain, resulting in only a handful of proposed algorithms over the last four decades. This study proposes the Prism Refraction Search (PRS), a novel, simple yet efficient, single-solution-based metaheuristic algorithm for single-objective real-parameter optimization. PRS is a physics-inspired algorithm modeled on a well-known optimization paradigm in ray optics arising from the refraction of light through a triangular prism. The key novelty lies in its scientifically sound background that is supported by the well-established laws of physical optics. The proposed algorithm is evaluated on several numerical objectives, including 23 classical benchmark functions, the CEC-2017 test suite, and five standard real-world engineering design problems. Further, the results are analyzed using standard statistical tests to prove their significance. Extensive experiments and comparisons with state-of-the-art metaheuristic algorithms in the literature justify the robustness and competitive performance of the PRS algorithm as a lightweight and efficient optimization strategy.